What are we talking about when we talk about algorithmic transparency?

The term ”algorithmic transparency”, with variants and variations, has become more and more common in the many conversations I have with decision makers and policy wonks. It remains somewhat unclear what it actually means, however. As a student of philosophy I find that there is often a lot of value in examining concepts closely in order to understand them, and in the following I wanted to open up a coarse-grained view of this concept in order to understand it further.

At a first glance it is not hard to understand what is meant with algorithmic transparency. Imagine that you have a simple piece of code that manipulates numbers, and that when you enter a series it produces an output that is another series. Say you enter 1, 2, 3, 4 and that the output generated is 1, 4, 9, 16. You have no access to the code, but you can infer that the codde probably takes the input and squares it. You can test this with a hypothesis – you decide to see if entering 5 gives you 25 in response. If it does, you are fairly certain that the code is something like ”take input and print input times input” for the length of the series.

Now, you don’t _know_ that this is the case. You merely believe so and for every new number you enter that seems to confirm the hypothesis your belief may be slightly corroborated (depending on what species of theory of science you subscribe to). If you want to know, really know, you need to have a peek at the code. So you want algorithmic transparency – you want to see and verify the code with your own eyes. Let’s clean this up a bit and we have a first definition.

(i) Algorithmic transparency means having access to the code a computer is running as to have a human be able to verify what it is doing.

So far, so good. What is hard about this, then, you may ask? In principle we should be able to do this with any system and so be able to just verify that it does what it is supposed to and check the code, right? Well, this is where the challenges start coming in.

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The first challenge is one of complexity. Let’s assume that the system you are studying has a billion lines of code and that to understand what the system does you need to review all of them. Assume, further, that the lines of code refer to each other in different ways and that there are interdependencies and different instantations and so forth – you will then end up with a situation where access to the code is essentially meaningless, because access does not guarantee verifiability or transparency in any meaningful sense.

This is easily realized by simply calculating the time needed to review a billion line piece of software (note that we are assuming her that software is composed of lines of code – not an obvious assumption as we will see later). Say you need one minute to review a line of code – that makes for a billion minutes, and that is a lot. A billion seconds is 31.69 years, so even if you assume that you can verify a line a second the time needed is extraordinary. And remember that we are assuming that _linear verification_ will be exhaustive – a very questionable assumption.
So we seem to have one interesting limitation here, that we should think about.

L1: Complexity limits human verifiability.

This is hardly controversial, but it is important. So we need to amend and change our definition here, and perhaps think about computer-assisted verification. We end up with something like.

(ii) Algorithmic transparency is achieved by access to the code that allows another system to verify the way the system is designed.

There is an obvious problem with this that should not be scooted over. As soon as we start using code to verify code we enter an infinite regress. Using code to verify code means we need to trust the verifying code over the verified. There are ways in which we can be comfortable with that, but it is worth understanding that our verification now is conditional on the verifying code working as intended. This qualifies as another limit.

So we are back to blind trust, but the choice we have is what system we have blind trust in. We may trust a system that we have used before, or that we believe we know more about the origins of, but we still need to trust that system, right?

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So, our notion of algorithmic transparency is turning out to be quite complicated. Now let’s add another complication. In our proto-example of the series, the input and output were quite simple. Now assume that the input consistens of trillions of documents. Let’s remain in our starkly simplified model: how do you know that the system – complex – is doing the right thing given the data?

This highlights another problem. What exactly is it that we are verifying? There needs to be a criterion here that allows us to state that we have achieved algorithmic transparency or not. In our naive example above this seems obvious, since what we are asking about is how the system is working – we are simply guessing at the manipulation of the series in order to arrive at a rule that will allow us to predict what a certain input will yield in terms of an output. Transparency reveals if our inferred rule is the right one and we can then debate if that is the way the rule should look. The value of such algorithmic transparency lies in figuring out if the system is cheating in any way.

Say that we have a game. I say that if you can guess what the next output will be and I show you the series 1, 2, 3, 4, and then the output 1, 4, 9, 16. Now I ask you to bet on what the next number will be as I enter 5. You guess 25 and I enter 5 and the output is 26. I win the bet. You require to see the code and the code says: ”For every input print input times input except if input is 5, then print input times input _plus one_”.

This would be cheating. I wrote the code. I knew it would do that. I put a trap in the code, and you want algorithmic transparency to be able to see that I have not rigged the code to my advantage. I am verifying two things: the rule I have inferred is the right one AND that rule is applied consistently. So it is the working of the system as well as its consistency or its lack of bias in anyway.

Bias or consistency is easy when you are looking at a simple mathematical series, but how do you determine consistency in a system that contains a trillion data points and uses a system of over, say, a billion lines of code? What does consistency mean? Here is another limitation, then.

L3: Algorithmic transparency needs to define criteria for verification such that they are possible to determine with access to the code and data sets.

I suspect this limitation is not trivial.

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Now, let’s complicate things further. Let’s assume that the code we use generates a network of weights that are applied to decisions in different ways, and that this network is trained by repeated exposure to data and its own simulations. The end result of this process is a weighted network with certain values across it, and perhaps they are even arrived at probabilistically. (This is a very simplified model, extremely so).
Here, by design, I know that the network will look different every time I ”train” it. That is just a function of its probabilistic nature. If we now want to verify this, what we are really looking for is a way to determine a range of possible outcomes that seem reasonable. Determining that will be terribly difficult, naturally, but perhaps it is doable. But at this point we start suspecting that maybe we are engaged with the issue at the wrong level. Maybe we are asking a question that is not meaningful.

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We need to think about what it is that we want to accomplish here. We want to be able to determine how something works in order to understand if it is rigged in some way. We want to be able explain what a system does, and ensure that what it does is fair, by some notion of fairness.

Our suspicion has been that what we need to do to do this is to verify the code behind the system, but that is turning out to be increasingly difficult. Why is that? Does that mean that we can never explain what these systems do?
Quite the contrary, but we have to choose an explanatory stance – to draw from a notion introduced by DC Dennett. Dennett, loosely, notes that systems can be described in different ways, from different stances. If my car does not start in the morning I can described this problem in a number of different ways.

I can explain it by saying that it dislikes me and is grumpy, assuming an _intentional_ stance, assuming that the system is intentional.
I can explain it by saying I forgot to fill up on gasoline yesterday, and so the tank is empty – this is a _functional_ or mechanical explanation.
I can explain it by saying that the wave functions associated with the care are not collapsing in such a way as to…or use some other _physical_ explanation of the car as a system of atoms or a quantum physical system.

All explanations are possible, but Dennett and others note that we would do well to think about how we choose between the different levels. One possibility is to look at how economical and how predictive an explanation is. While the intentional explanation is shortest, it gives me now way to predict what will allow me to change the system. The mechanical or functional explanation does -and the physical would take pages on pages to do in a detailed manner and so is clearly uneconomical.
Let me suggest something perhaps controversial: the ask for algorithmic transparency is not unlike an attempt at explaining the car’s malfunctioning from a quantum physical stance.
But that just leaves us with the question of how we achieve what arguably is a valuable objective: to ensure that our systems are not cheating in any way.

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The answer here is not easy, but one way is to focus on function and outcomes. If we can detect strange outcome patterns, we can assume that something is wrong. Let’s take an easy example. Say that an image search for physicist on a search engine leads to a results page that mostly contains white, middle-aged men. We know that there are certainly physicists that are neither male or white, so the outcome is weird. We then need to understand where that weirdness is located. A quick analysis gives us the hypothesis that maybe there is a deep bias in the input data set where we, as a civilization, have actually assumed that a physicist is a white, middle-aged man. By only looking at outcomes we are able to understand if there is bias or not, and then form hypothesis about where that bias is introduced. The hypothesis can then be confirmed or disproven by looking at separate data sources, like searching in a stock photo database or using another search engine. Nowhere do we need to, or would we indeed benefit from, looking at the code. Here is another potential limitation, then.

Outcome analysis also has the advantage of being openly available to anyone. The outcomes are necessarily transparent and accessible, and we know this from a fair amount of previous cases – just by looking at the outcomes we can have a view on whether a system is inherently biased or not, and if this bias is pernicious or not (remember that we want systems biased against certain categories of content, to take a simple example).

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So, summing up. As we continue to explore the notion of algorithmic transparency, we need to focus on what it is that we want to achieve. There is probably a set of interesting use cases for algorithmic transparency, and more than anything I imagine that the idea of algorithmic transparency actually is an interesting design tool to use when discussing how we want systems to be biased. Debating, in meta code of some kind, just how bias _should be_ introduced in, say, college admission algorithms, would allow us to understand what designs can accomplish that best. So maybe algorithmic transparency is better for the design than the detection of bias?

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This is where you find the unfinished thoughts and sketches, this is where we can debate and discuss. This is where the marginal notes, the hunches and investigations end up. This is where I am often wrong. This is where I speak only for myself, and sometimes not even that – I change my mind fairly often.